启发式牙颌CT影像自动分割

准确快速地分割CT切片特征轮廓是医学图像三维重建的重要环节。现有的轮廓分割方法必须通过手动层层交互操作,不仅耗时而且分割精度不高。针对这种局限性,提出一种基于启发式牙颌CT影像自动分割方法。首先用拉普拉斯算子对CT图像序列进行边缘增强,其次用轮廓匹配映射技术实现轮廓启发式传递,最后基于收缩包围算法自动分割牙颌序列。以14例完整牙（每例28～32颗牙数据样本）锥束CT断层扫描图像序列进行实验,在相同条件下分别用所提出的轮廓自动提取方法和其他提取方法,对实验样本进行轮廓提取,得到单颗牙轮廓提取的平均用时和提取轮廓与真实轮廓之间的距离差平均值。实验结果显示,轮廓自动分割算法提取单颗牙轮廓的用时约为其他手工分割法提取单颗牙轮廓用时的23%,同时提取的轮廓质量和用传统方法提取的轮廓质量相当。该方法为CT数据特征区自动化分割提供一种可行且高效的方法,为进一步改进现有的CT影像分割和三维重建算法提供了新的思路。     

用于面诊的人脸全景图像拼接算法

采用一种基于人脸特征的柱面投影方法,将人的头部近似看做一个圆柱体,有效地解决了在采集过程中因面部角度所引起的视觉不一致性;利用SIFT特征匹配算法提取两幅图像的特征向量,并通过RANSAC匹配优化算法消除错误的匹配,实现图像的配准;接着采用渐入渐出的融合算法,使图像间实现平滑的过渡,消除拼接缝隙。实验结果表明,本研究使用的算法能够快速、有效地生成人脸全景图像,为后续中医面诊奠定了基础。     

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.     

基于自由变形法的多模态医学图像的配准与融合

本研究提出了一种自动识别颈部PET-CT图像特征点的算法，它应用自由变形（FFD）方法以CT图像的特征点为参考使PET图像产生变形，再结合最大互信息法对颈部PET与CT图像进行非刚体配准，最后用改进的小波图像融合法把两者进行融合得出视觉效果比较理想的融合图像。经实际计算得出的变形PET图像与对应CT图像的互信息量大于原始PET图像，并且最后用改进的小波图像融合法得出的融合图像的信息量比一般小波融合大，由此证明本研究所用方法是有效的。     

灰度与形状同步匹配的非刚性配准研究

基于灰度的非刚性配准算法一般假设参考图像和浮动图像对应结构之间的灰度保持一致,然而在基于图谱的图像配准应用中,这种假设往往不符合实际。本文在给出一种可以同时校正灰度和形状差异的弹性配准算法的同时,针对该算法不能校正局部微小形变的弱点,提出采用自由项变换的方法进行校正以提高配准精度。配准实验基于20个IBSR真实脑部MRI图像,结果表明配准后图像与参考图像间的互相关系数得到明显提高。实验证明,本文提出的方法不仅能够同时校正形状差异和灰度变化,而且具有较高的配准质量。     

一种基于感兴趣点旋转不变性特征的图像配准新方法

目的:图像配准是图像处理领域重要的研究方向,是图像融合、图像重建和图像分析等研究的基础。在图像配准的主要方法中,基于图像特征的配准方法和基于图像灰度的配准方法各有优缺点,通过结合这两种方法的优点,我们提出了一种基于感兴趣点的旋转不变性特征图像配准的新方法。方法:首先利用Harris角点检测技术,提取模板图像和目标图像的感兴趣点。然后把感兴趣点的旋转不变形特征和灰度值组成图像的特征描述向量,并提出新的代价函数。最后采用分级优化的策略优化代价函数,在配准初期,采用显著的特征点进行配准,以保证配准的速度与鲁棒性,随后通过逐步增加特征点的数量,则保证了配准的精度。结果:为显示本文方法的优越性,实验利用本文方法和基于互信息的B样条方法分别对标准测试图像进行配准,实验结果表明,本文方法较基于互信息的B样条方法在配准精度上有明显提高。结论:本文方法在保持配准鲁棒性的前提下,获得了较高的配准精度。     

基于特征提取的脑部医学图像配准研究

目的 基于特征的配准算法具有鲁棒性强、针对性好等显著优势,在图像配准领域被广泛应用,但是该类方法的精度受图像间特征构建和环境噪声影响大,该研究旨在对其缺点进行改进。方法 该研究基于SURF和ORB两种算法,提出了SURF-ORB算法,将参考图像与待配准图像分成上下两部分分别配准。在配准过程中,首先对SURF提取的图像特征点的Harris响应值进行优化,并使用灰度质心法确定特征点主方向。然后计算rBRIEF（旋转BRIEF）描述子,并使用汉明距离进行特征点匹配。最后加入RANSAC精匹配算法,剔除误匹配点。结果和结论 该研究通过对比分析SURF、ORB、SURF-ORB这3种算法的配准结果、抗噪声能力及多模态配准能力,验证了SURF-ORB算法具有较高的配准精度、配准速度和抗噪声能力。文章的创新之处该研究首次将SURF和ORB两种算法进行结合并应用于脑部横断面图像。     

虚拟中国人女性一号松质骨图像数据的配准与三维重建

目的：研究从虚拟人体数据集中松质骨连续切片图像的分割、配准、及三维重建的技术方法。方法：利用现有的虚拟中国人女性一号数据集中腰椎和股骨部分解剖连续切片数据集，用基于外置标记点和分割—计数法两种方法进行参数计算，依参数对图像进行刚体变换完成配准，将配准后的切片图像输入二维图像处理软件进行分割，提取感兴趣区域后输入三维重建软件进行三维重建。结果：重建后的松质骨三维立体图像呈均匀、致密的立体网状结构，骨小梁连接清晰可见。结论：利用现有软件及技术可重建虚拟人体的精细结构。     

医学图像弹性配准中弹性模型的建立

医学图像配准是医学图像处理中的一个重要研究课题，它是图像融合、图像与标准图谱的匹配、显微图像的重建等研究的基础。图像的配准方法有多种，它们可以分为刚性和弹性配准两大类。相对于刚性配准，弹性配准有着更高的精确性，而对于变形大的图像的配准，它是必须的。因此弹性配准的研究有着广泛的意义。本文根据图像的特征，结合弹性力学的理论和方法，建立了一种用于精确配准的弹性数学模型，并用这一模型进行图像的弹性配准，取得了较好的效果。     

基于薄板样条的医学图像弹性配准

非刚体配准是神经外科和放疗计划设汁中的一个关键问题。使用薄板样条方法，利用两个对应标志点集对眼底、脑等多种医学图像进行弹性配准。其中，弹性插值法是将两个点集绝对对齐，常会出现严重的局部畸变；而弹性近似法充分考虑了整体平滑性的要求，对定位有误差的标志点约束的图像配准更为优越。实验结果表明，使用上述两种方法获得了很好的配准效果?     

Three-dimensional reconstruction and quantitative analysis of the brain stem nuclei based on fast centroid auto-registration

This paper introduces a three-dimensional (3D) reconstruction algorithm of the brain stem nuclei based on fast centroid auto-registration. The research is based on methods and theories of computer stereo vision, and by image information processing three-point pattern local search, registration and auto-tracing for the centroids of the brain stem nuclei were accomplished. We adopt two-peak threshold, edge detection and grayscale image enhancement to extract contours of the nuclei's structures. The experimental results obtain the spatial structure information and 3D image of the brain stem nuclei, show spatial relationship between 14 pairs of nuclei, and quantitate morphological parameters of each type of nuclei's 3D structure. This work is significant to neuroanatomy research and clinic applications. Furthermore, a software system named BRAIN.HUK is established.     

大鼠松质骨切片图像的三维重建与定量分析

本文研究动物松质骨连续切片图像数据集的获取、分割、配准、及三维重建的技术方法.利用病理切片和图像数码摄入技术,获取了大鼠腰椎松质骨连续切片图像数据集,用基于外置标记点和分割-计数法两种方法进行参数计算,依参数对图像进行刚体变换完成配准,将配准后的切片图像输入二维图像处理软件进行分割,提取感兴趣区域后输入三维重建软件进行三维重建与定量分析.重建后的松质骨三维立体图像呈均匀、致密的立体网状结构,骨小梁连接清晰可见.     

基于先验知识和MRF随机场模型的医学图像弹性配准方法

本研究提出了一种新的基于先验知识的弹性配准算法，首次把马尔可夫模型应用于图像的弹性配准方面。为了把关于变形场的先验知识融合到弹性配准过程中，本研究以马尔可夫随机场模型作为理论框架，以B样条为基函数来构造弹性变形模型，以弹性模型的B样条系数作为待估参数，以原图像和变形图像作为已知条件，把弹性变形模型和关于变形场的先验知识有机的融合到了马尔可夫随机场模型中，实现了一种基于变形场先验知识的弹性配准算法。这种新算法因为有变形场的先验知识，所以可以得到更好配准结果。本研究以变形场的平滑作为先验知识，可以有效改善局部极值的状况，提高算法的可靠性和鲁棒性。本研究分别对2D和3D图像进行了试验，试验结果证明了这种算法的有效性。     

基于FCM聚类算法与自适应阈值的冠脉钙化分割与量化

针对基于CTA图像进行冠脉钙化量化时存在的无法克服噪声以及阈值选择不稳定问题,提出一种基于聚类算法与自适应阈值的冠脉钙化分割与量化方法。首先根据CT值和空间位置对冠脉血管内的像素点构建特征向量,继而根据血管骨架点数目构建自适应聚类数,使用模糊C均值(FCM)聚类算法将冠脉区域划分为CT值分布相似的区域;然后使用高斯函数拟合冠脉灰度直方图,根据高斯拟合参数构造自适应阈值,对上述区域进行钙化分割;最后根据分割结果,参考Agatston钙化分量化标准进行钙化分计算。在30组人体冠脉CTA数据的测试结果中,对冠脉钙化量化的灵敏度和特异性分别达到89.5%与98.6%,计算得到的钙化体积和Agatston钙化分与标准结果的皮尔逊系数分别为0.974与0.975,远高于同类型基于一阶微分进行阈值选择方法(DBTD)对应的0.523与0.501。 实验结果表明,该方法可用于冠脉钙化分割与量化,且具有全自动、鲁棒性好、能有效抗噪等特点。     

基于CT数据的股骨轴线的获取

目的　提出一种利用CT影像数据自动获取股骨轴线的方法,利用该方法可以在计算机上获取精准的股骨三维解剖轴线与机械轴线。方法　对人体下肢进行CT扫描,然后对CT图像进行二维阈值分割、三维数据体重建、骨骼分组、数据平滑填充及三维数据体旋转等预处理,获得三维股骨表面模型;通过对股骨头与股骨远端形态特征的分析,根据股骨头近似球形的特点,对判断断层扫描数据梯度变化来计算股骨头中心位置,另外,逐层搜索膝关节断层图像上闭环区域的方法自动求得股骨远端中心的坐标,从而获得股骨长轴。结果　建立了一种基于CT图像简便快捷的自动获取股骨头中心与膝关节骨中心三维空间坐标的方法。结论　通过此方法可较为精确获得人体下肢的三维股骨机械轴线和股骨解剖轴线。     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching

The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.     

基于B样条的医学图像弹性配准

为解决医学图像上的形变和偏移,本研究采用基于B样条自由形变的配准方法.建立一个由控制点组成的网格,并将其套用在待配准图像上,得到初始控制网格参数.以梯度下降法为优化算法,以SSD测度为相似性测度,不断修正该参数,参数的改变将引起控制点最近邻4×4控制网格内所有的像素点发生移动,移动后的坐标位置由B样条拟合得到.以双线性插值为插值算法,计算出该位置的灰度值,该位置相对于配准后图像,等同于像素点移动前的位置映射到参考图像相对应的位置,当配准后图像与参考图像对应位置的灰度差异度达到最小时,就达到图像的最佳配准.实验结果表明,该算法配准结果满意,从而验证了本研究算法的有效性.     

Image registration with auto-mapped control volumes

Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of inhale and exhale phases of a lung 4D CT. Algorithm convergence was confirmed by starting the registration calculations from a large number of initial transformation parameters. An accuracy of approximately 2 mm was achieved for both deformable and rigid registration. The proposed image registration method greatly reduces the complexity involved in the determination of homologous control points and allows us to minimize the subjectivity and uncertainty associated with the current manual interactive approach. Patient studies have indicated that the two-step registration technique is fast, reliable, and provides a valuable tool to facilitate both rigid and nonrigid image registrations.     

一种医学图像几何特征的自动检测

医学图像配准的方法有许多种，它的原理都是选取合适的图像特征量，根据特征量再来确定配准变换，因此特征量的选取是图像配准的第一步。目前用于配准的图像特征量有多种，如图像边界、图像外标记点等。本文提供了一种提取图像脊特征，作为用于配准的特征量的方法，取得了良好效果。